Speaker identification using Ultra‐Wideband measurement of voice
Abstract Voice identification is being increasingly adopted in various domains, including security infrastructures, intelligent home systems, and personalised digital assistants. Notably, it harbours significant promise in transforming healthcare, especially in electronic health record detecting and...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-02-01
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| Series: | IET Radar, Sonar & Navigation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rsn2.12525 |
| _version_ | 1797301303526817792 |
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| author | Haoxuan Li Chong Tang Shelly Vishwakarma Yao Ge Wenda Li |
| author_facet | Haoxuan Li Chong Tang Shelly Vishwakarma Yao Ge Wenda Li |
| author_sort | Haoxuan Li |
| collection | DOAJ |
| description | Abstract Voice identification is being increasingly adopted in various domains, including security infrastructures, intelligent home systems, and personalised digital assistants. Notably, it harbours significant promise in transforming healthcare, especially in electronic health record detecting and speech impairment monitoring such as aphasia. Current strategies such as acoustic models based on deep learning, voice bio‐metrics, and spectrogram analysis, have been identified with several drawbacks including vulnerability to altered voices, susceptibility to ambient noise, and the necessity for significant computational power. In response to these issues, the authors introduce a ground‐breaking method of voice identification using Ultra‐Wideband (UWB) technology. This method capitalises on the micro‐Doppler shifts associated with movements of the laryngeal prominence. The distinctive nature of these bio‐metric traits related to speech production provides superior resistance against common pitfalls of voice identification. The proposed model leverages the high‐resolution characteristics of UWB to register tiny variations in laryngeal movements produced during speech, thus forming a distinct voice profile for each speaker. Through rigorous testing, the proposed system demonstrated significant progress in voice identification, achieving close to 90% accuracy in controlled experimental settings. This breakthrough indicates that UWB‐enabled voice identification could have a profound effect on medical applications, providing potential improvements in diagnosing, monitoring, possibly treating speech disorders, and thereby shaping a future of enhanced and secured healthcare services. |
| first_indexed | 2024-03-07T23:19:38Z |
| format | Article |
| id | doaj.art-1780aac553f64a0f93feeb84784c07b5 |
| institution | Directory Open Access Journal |
| issn | 1751-8784 1751-8792 |
| language | English |
| last_indexed | 2024-03-07T23:19:38Z |
| publishDate | 2024-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Radar, Sonar & Navigation |
| spelling | doaj.art-1780aac553f64a0f93feeb84784c07b52024-02-21T06:53:09ZengWileyIET Radar, Sonar & Navigation1751-87841751-87922024-02-0118226627610.1049/rsn2.12525Speaker identification using Ultra‐Wideband measurement of voiceHaoxuan Li0Chong Tang1Shelly Vishwakarma2Yao Ge3Wenda Li4Department of Biomedical Engineering University of Dundee Dundee UKDepartment of Electronics and Computer Science University of Southampton Southampton UKDepartment of Electronics and Computer Science University of Southampton Southampton UKJames Watt School of Engineering University of Glasgow Glasgow UKDepartment of Biomedical Engineering University of Dundee Dundee UKAbstract Voice identification is being increasingly adopted in various domains, including security infrastructures, intelligent home systems, and personalised digital assistants. Notably, it harbours significant promise in transforming healthcare, especially in electronic health record detecting and speech impairment monitoring such as aphasia. Current strategies such as acoustic models based on deep learning, voice bio‐metrics, and spectrogram analysis, have been identified with several drawbacks including vulnerability to altered voices, susceptibility to ambient noise, and the necessity for significant computational power. In response to these issues, the authors introduce a ground‐breaking method of voice identification using Ultra‐Wideband (UWB) technology. This method capitalises on the micro‐Doppler shifts associated with movements of the laryngeal prominence. The distinctive nature of these bio‐metric traits related to speech production provides superior resistance against common pitfalls of voice identification. The proposed model leverages the high‐resolution characteristics of UWB to register tiny variations in laryngeal movements produced during speech, thus forming a distinct voice profile for each speaker. Through rigorous testing, the proposed system demonstrated significant progress in voice identification, achieving close to 90% accuracy in controlled experimental settings. This breakthrough indicates that UWB‐enabled voice identification could have a profound effect on medical applications, providing potential improvements in diagnosing, monitoring, possibly treating speech disorders, and thereby shaping a future of enhanced and secured healthcare services.https://doi.org/10.1049/rsn2.12525Biometric identificationResNetSpeaker identificationUWB radarVoice recognition |
| spellingShingle | Haoxuan Li Chong Tang Shelly Vishwakarma Yao Ge Wenda Li Speaker identification using Ultra‐Wideband measurement of voice IET Radar, Sonar & Navigation Biometric identification ResNet Speaker identification UWB radar Voice recognition |
| title | Speaker identification using Ultra‐Wideband measurement of voice |
| title_full | Speaker identification using Ultra‐Wideband measurement of voice |
| title_fullStr | Speaker identification using Ultra‐Wideband measurement of voice |
| title_full_unstemmed | Speaker identification using Ultra‐Wideband measurement of voice |
| title_short | Speaker identification using Ultra‐Wideband measurement of voice |
| title_sort | speaker identification using ultra wideband measurement of voice |
| topic | Biometric identification ResNet Speaker identification UWB radar Voice recognition |
| url | https://doi.org/10.1049/rsn2.12525 |
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